The present invention relates to learning of landmark detection models for landmark detection in medical images, and more particularly, to on-site learning of landmark detection models for end user-specific diagnostic medical image reading.
In radiology, routine reading of three dimensional medical images, e.g., from computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), etc., usually follows fixed rules and protocols. Such protocols are sometimes established and re-fined by individual hospitals on their own behalf to ensure and to achieve a high quality of care. Also, physicians often develop their own methodology for a systematic reading of cases. Depending on the medical question to be answered, navigation through the data sets may require the inspection of certain important anatomical landmarks, in terms of 3D coordinates, or their surroundings. Standardized tomographic 2D views, i.e., planes, in the 3D medical image data may have to be oriented in relation to such anatomical landmarks. Furthermore, semantic information about landmark locations in specific patient data sets may be used, often in combination with patient-specific information from other sources, for higher-level image post-processing like image fusion, content-based image retrieval, or knowledge-based decision support.
However, manual navigation to particular anatomical landmarks and double oblique alignment of 2D planes to the anatomical landmarks is time consuming and the radiologists' workflow can significantly be improved by automatic detection of landmarks and corresponding oriented planes. Algorithms for automatic detection of landmarks and oriented planes are typically tied to specific clinical questions and modalities. Different or newly developed radiological examinations and workflows may require different landmarks and alternatively oriented view planes in various imaging modalities and contexts. Furthermore, hospitals and groups of physicians all over the world may have their own individual preferences, leading to greater challenges in automating landmark detecting and image orientation.